Various Doppler radar techniques are known in the art. Examples are taught in the following references, which are hereby incorporated in their entirety by reference into this specification:
U.S. Pat. No. 5,027,122 to Wieler, Jun. 25, 1991;
U.S. Pat. No. 5,122,805 to Peterman et al, Jun. 16, 1992;
U.S. Pat. No. 5,173,704 to Buehler et al, Dec. 22, 1992;
Chadwick, R. B., A. S. Frisch, and R. G. Strauch, 1984: A feasibility study on the use of wind profilers to support space shuttle launches. NASA contractor report, 3861.
Gage, K. S., and B. B. Balsley, 1978: Doppler radar probing of the clear atmosphere. Bull. Amer. Meteor. Soc., 59, 1074-1093.
Gauthreaux, Jr., S. A., 1991: The flight behavior of migrating birds in changing wind fields: radar and visual analyses. Amer. Zool., 31, 187-204.
Gossard, E. E., and R. G. Strauch, 1983: Radar observations of clear air and clouds. Developments in Atmospheric Science, 14, Elsevier Science, New York, 280 pp.
Hildebrand, P. H., and R. S. Sekhon, 1974: Objective determination of the noise level in Doppler spectra. J. Appl. Meteor., 13, 808-811.
Hoehne, W. E., 1980: Precision of National Weather Service upper air measurements. NOAA Tech. Memo. NWS T&ED-16, 12 pp.
Knight, C. A., and L. J. Miller, 1993: First radar echoes from cumulus clouds. Bull. Amer. Meteor. Soc., 74, 179-188.
May, P. T., 1993: Comparison of Wind Profiler and Radiosonde Measurements in the Tropics. J. Atmos. Ocean. Technol., 10, 122-127.
Moran, K. P., R. G. Strauch, K. B. Earnshaw, D. A. Merritt, B. L. Weber, and D. B. Wuertz, 1989: Lower tropospheric wind profiler. 24th Conference on radar meteorology, Mar. 27-31, 1989, Tallahassee, Fla., AMS, Boston, Mass., 729-731.
Nathanson, F. E., 1969: Radar design principles. McGraw Hill, 626 pp.
Ottersten, H., 1969: Atmospheric structure and radar backscattering in clear air. Radio Sci., 4, 1179-1193.
Strauch, R. G., D. A. Merritt, K. P. Moran, K. B. Earnshaw, and D. van de Kamp, 1984: The Colorado wind-profiling network. J. Atmos. Ocean. Technol., 1, 37-49.
Strauch, R. G., B. L. Weber, A. S. Frisch, C. G. Little, D. A. Merritt, K. P. Moran, and D. C. Welsh, 1987: The precision and relative accuracy of profiler wind measurements. J. Atmos. Ocean. Techhnol., 4, 563-571.
Vaughn, C. R., 1985: Birds and insects as radar targets: A review. Proc. IEEE, 73, 205-227.
Weber, B. L., D. B. Wuertz, R. G. Strauch, D. A. Merritt, K. P. Moran, D. C. Law, D. van de Kamp, R. B. Chadwick, M. H. Ackley, M. F. Barth, N. L. Abshire, P. A. Miller, and T. W. Schlatter, 1990: Preliminary evaluation of the first NOAA demonstration network wind profiler. J. Atmos. Ocean. Technol., 7, 909-918.
Weber, B. L., D. B. Wuertz, D. C. Law, A. S. Frisch, and J. M. Brown, 1992: Effects of small-scale vertical motion on radar measurements of wind and temperature profiles. J. Atmos. Ocean. Technol., 9, 193-209.
Weber, B. L., D. B. Wuertz, D. C. Welsh, and R. McPeek, 1993: Quality controls for profiler measurements of winds and RASS temperatures. J. Atmos. Ocean. Technol., 10, 452-464.
Wesely, M. L., 1976: The combined effect of temperature and humidity fluctuations on refractive index. J. Appl. Meteor., 15, 43-49.
Wilczak, J. M., R. G. Strauch, F. M. Ralph, B. L. Weber, D. A. Merritt, J. R. Jordan, D. E. Wolfe, L. K. Lewis, D. B. Wuertz, J. E. Gaynor, S. A. McLaughlin, R. R. Rogers, A. C. Riddle, and T. S. Dye, 1994: Contamination of wind profiler data by migrating birds: Characteristics of corrupt bird data and potential solutions. (in review)
Wuertz, D. B., B. L. Weber, R. G. Strauch, A. S. Frisch, C. G. Little, D. A. Merritt, K. P. Moran, and D. C. Welsh, 1988: Effects of precipitation on UHF wind profiler measurements. J. Atmos. Ocean. Technol., 5, 450-465.
Since Gage and Balsley (1978) summarized Doppler radar capability for probing the atmosphere, wind profiling radars have been used successfully for meteorological research and they have been considered for routine operations (Strauch et al, 1984). More recently, profilers were deployed in the NOAA Demonstration Network for evaluation by the National Weather Service (Weber et al., 1990). Profilers are expected to have a growing impact upon weather forecasting, environmental pollution monitoring, climate and mesoscale modelling, air traffic control, and more.
It is important, therefore, that the wind measurements of these radars be both accurate and reliable. Strauch et al. (1987) showed that in clear air, small-scale meteorological variability probably limits the precision of profilers, being not much different from that of conventional radiosondes (Hoehne, 1980). At UHF frequencies, measurement errors can become large in the presence of precipitation, but when appropriate methods are used a profiler can use the much stronger radar return from precipitation to its advantage (Wuertz et al., 1988). At VHF frequencies, May (1993) found little effect on observations due to precipitation. More recent studies suggest that accuracy and reliability are improved with high-resolution sampling using five-beam antenna systems (Weber et al., 1992) and with improved data processing and quality controls (Weber et al., 1993).
Nevertheless, it is now recognized that profilers sometimes have large measurement errors in the presence of radar returns from unwanted targets. Profilers are especially sensitive to contamination from other targets whose radar echoes can be orders-of-magnitude stronger than the clear-air return. Ground clutter, sea clutter, and echoes from precipitation or clouds can enter low-angle antenna sidelobes, producing signals that are sometimes mistaken for atmospheric signal in the main antenna beam. Meanwhile, aircraft, insects, and birds can fly directly through the main antenna beam, producing spectral contamination much stronger than the atmospheric signal.
UHF radars that are used for profiling the wind in the atmosphere must be extremely sensitive in order to detect the very weak backscatter from index-of-refraction fluctuations caused by turbulence in clear air. Ottersten (1969) showed that the radar reflectivity of clear air is .eta..apprxeq.0.38 C.sup.2.sub.n .lambda..sup.-1/3 where .lambda. is the radar wavelength and where C.sup.2.sub.n is the refractive index structure function parameter for the atmosphere. Typical values for C.sup.2.sup.n in the lower troposphere range from 10.sup.-15 to 10.sup.-13 m.sup.-2/3, although larger values up to 10.sup.-11 m.sup.-2/3 have been observed (Gossard and Strauch, 1983; Knight and Miller, 1993). Much smaller values are generally observed in the upper atmosphere where the air is drier. Wesely (1976) relates C.sup.2.sub.n to the scalar structure function parameters for temperature and humidity, showing that at microwave frequencies humidity has the greater influence.
The radar return from aircraft, birds, and insects can be much stronger than the radar return from clear-air turbulence. Even though radar observations of birds and insects are not new (Vaughn, 1985), the potential problem for profilers posed by flying objects was perhaps underestimated. However, recently the remote sensing community has come to realize that profiler performance is degraded at times by contamination from migrating birds to a far greater extent than was previously expected (Wilczak et al., 1994). Included in this are all organic fliers, including bats and insects, but it is the large number of migrating birds that has caught the attention of the radar community. The problem is aggravated by smaller antennas with their broad beams and high sidelobe levels. Because of this, the high-frequency boundary-layer radars were the first to draw wide attention to this problem. Then it was realized that the NOAA profilers (404 and 449 MHz) are also very susceptible to interference from bird echoes.
Bird echoes (and echoes from other fliers) can be mistaken for the atmospheric signal, producing spurious measurements with no meteorological information content. Spurious measurements are harmful for two reasons: (1) they can inject misleading information into an application and (2) they prevent meaningful measurements from being made. The occasional isolated flier produces contamination in the Doppler spectra that is sometimes mistaken for atmospheric signal, producing erroneous wind measurements. The erroneous winds can often be identified and removed at later stages of radar signal processing, although that is by no means guaranteed. The contamination is much more serious and widespread with migrating birds, which can reach concentrations of over 100,000 birds crossing a mile-wide front every hour. These migrating birds normally use favorable tail winds and calmer weather conditions to their advantage (Gauthreaux, 1991). As a result, their velocities tend to blend in with surrounding wind velocities, sometimes making the identification of bird-contaminated wind measurements difficult or impossible. Therefore, it is necessary to identify and remove this contamination before the spectral estimation is made.
With Doppler radars, different targets can sometimes be identified by differences in their velocities. However, even when different targets have nearly the same velocities those targets can still be separated based upon their unique scattering properties. Hildebrand and Sekhon (1974) used this principle to identify the system noise in each Doppler spectrum. Noise and atmospheric echoes from rain and the clear air have different Gaussian statistical distributions. Aircraft and bird echoes, on the other hand, are expected to exhibit different statistical distributions altogether.
In typical profiling radars, many Doppler spectra are averaged over some dwell time for every antenna beam and for every range gate. The averaging is intended to improve signal detection by reducing the noise fluctuations in the spectra, i.e., by smoothing the spectra. If some of the spectra are contaminated, then the average spectrum is also contaminated. One way to eliminate the contamination in the average is by editing the data before averaging. An example of this is the conventional consensus averaging used for years to produce hourly winds with profilers. One might be tempted to use a consensus average or a median estimator rather than a simple average on the spectra, and the inventor did, in fact, use a median estimator with some success. The problem with both the median and the consensus methods, however, is that they depend upon the desired data constituting a majority of the data with the contamination being in the minority.